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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets"

共 9193 条记录，以下是81-90 订阅

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LAVViT: Latent Audio-Visual Vision Transformers for Speaker Verification

LAVViT: Latent Audio-Visual Vision Transformers for Speaker ...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： R. Gnana Praveen Jahangir Alam Computer Research Institute of Montreal (CRIM) Montreal Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, Vision Transformers (ViTs) have shown remarkable success in various computer vision applications. In this work, we have explored the potential of ViTs, pre-trained on visual data, for audio-visual speaker verification. To cope with the challenges of large-scale training, we introduce the Latent Audio-Visual Vision Transformer (LAVViT) adapters, where we exploit the existing pre-trained models on visual data without fine-tuning their parameters and train only the parameters of LAVViT adapters. The LAVViT adapters are injected into every layer of the ViT architecture to effectively fuse the audio and visual modalities using a small set of latent tokens, forming an attention bottleneck, thereby reducing the quadratic computational cost of cross-attention across the modalities. The proposed approach has been evaluated on the Voxceleb1 dataset and shows promising performance using only a few trainable parameters. Code is available at https://***/praveena2j/LAVViT

关键词： Training Visualization Computer vision Fuses Computer architecture signal processing Transformers data models Computational efficiency Speech processing

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Ultrasound-Guided Registration Pseudo-Labels for Semi-Supervised Brachial Plexus Segmentation

Ultrasound-Guided Registration Pseudo-Labels for Semi-Superv...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jia Luo Ze Zhang Yi Ding Yiqian Wang Jian Zhang Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Yibin GREAT Technology Co. Ltd Yibin China Sichuan Provincial Hospital for Women and Children/Affilated Women and Children’s Hospital of Chengdu Medical College Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In semi-supervised medical image segmentation, two main challenges arise. First, the quality of pseudo-labels generated by segmentation networks in data-limited scenarios is often poor, reducing segmentation accuracy. Second, many methods fail to effectively utilize the temporal context in video data. Moreover, the intricate relationships among multiple targets make the conventional teacher-student network consistency loss unsuitable for multi-target tasks, leading to inaccurate feature capture and degraded performance. To address these issues, we propose a registration-based pseudo-label generation strategy that produces pseudo-labels more closely aligned with actual labels. Additionally, we introduce multiple guidance mechanisms into the teacher-student network to ensure correct optimization. Experimental results show that our approach significantly outperforms leading semi-supervised segmentation methods. Specifically, at a 50% label rate, our method improves the Dice coefficient by 6.9% over existing methods.

关键词： Deformable models Image segmentation Accuracy Ultrasonic imaging Deformation signal processing Brachytherapy Speech processing Optimization Biomedical imaging

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The Evolution of System on Chip Integrated Circuits for Hearing-Aid signal processing

The Evolution of System on Chip Integrated Circuits for Hear...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： James Ryan Ivo Coenen Robert Brennan Onsemi Waterloo Canada Onsemi Switzerland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Integrated circuits used in modern, digital hearing aids are mixed-signal devices that combine analog and digital circuits on the same die. Due to the extreme size and power constraints encountered in hearing aids, the assignment of die area for such systems-on-chip (SOCs) must be carefully considered. The relative allocation between analog circuits, digital gates and on-chip memory has been relatively constant for traditional DSP-based systems. The precision and computational requirements for artificial intelligence (AI) based algorithms, however, are very different from traditional DSP. This is driving changes in die area allocation, starting with some recently launched devices. Despite these changes, projections based on recent growth trends show that future SOCs will only be able to support the smallest neural networks currently proposed for hearing aids. Minimizing data precision and pruning small-valued weights are shown to be essential to deploy deep-learning algorithms on hearing aid SOCs.

关键词： Integrated circuits signal processing algorithms Random access memory signal processing Hearing aids System-on-chip Resource management Artificial intelligence Speech processing System analysis and design

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APLASE: Compression using Adaptive Piecewise Linear Approximation and Sparse Encoding

APLASE: Compression using Adaptive Piecewise Linear Approxim...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ravi Raj Saxena T.V. Prabhakar Joy Kuri Chandra R. Murthy Dept. of Electronic System Engineering Indian Institute of Science Bengaluru India Dept. of Electrical Communication Engineering Indian Institute of Science Bengaluru India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This work focuses on compressing vast amounts of time series data from IoT sensors while achieving low reconstruction error, at a low compression ratio (ratio of output data size to input data size), for efficient storage and transmission. We investigate two lossy compression techniques: Adaptive Piecewise Linear Approximation (APLA) and Sparse Encoding (SE). APLA reduces data volume by leveraging the temporal correlation in time series data, while SE captures small, complex variations that APLA might miss. We identify a signal characteristic parameter, denoted by σ d , which is used to derive error bounds for SE using σ d , the dictionary used, and the input compression parameters. We also introduce APLASE, a novel compression technique that combines the two techniques by applying SE to the reconstruction error generated by APLA. We evaluate APLA, SE and APLASE using data from the NASA flight data recorder database consisting of recordings from 116 sensors. Our results show that APLASE consistently outperforms both APLA and SE individually, as well as the recent QoZ algorithm, particularly at lower compression ratios.

关键词： Measurement Dictionaries Time series analysis Piecewise linear approximation NASA signal processing algorithms Encoding Sensors Speech processing Standards

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Ultra-Low Latency Speech Enhancement -A Comprehensive Study

Ultra-Low Latency Speech Enhancement -A Comprehensive Study

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haibin Wu Sebastian Braun National Taiwan University WA USA Microsoft Redmond WA USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech enhancement models should meet very low latency requirements typically smaller than 5 ms for hearing assistive devices. While various low-latency techniques have been proposed, comparing these methods in a controlled setup using DNNs remains blank. Previous papers have variations in task, training data, scripts, and evaluation settings, which make fair comparison impossible. Moreover, all methods are tested on small, simulated datasets, making it difficult to fairly assess their performance in real-world conditions, which could impact the reliability of scientific findings. To address these issues, we comprehensively investigate various low-latency techniques using consistent training on large-scale data and evaluate with more relevant metrics on real-world data. Specifically, we explore the effectiveness of asymmetric windows, learnable windows, adaptive time domain filterbanks, and the future-frame prediction technique. Additionally, we examine whether increasing the model size can compensate for the reduced window size.

关键词： Training Measurement Time-frequency analysis Training data Transforms Speech enhancement signal processing Reliability Low latency communication Time-domain analysis

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Meta-Analogy Learning Based on Dynamic Graph Neural Networks for Inductive Knowledge Graph Link Prediction

Meta-Analogy Learning Based on Dynamic Graph Neural Networks...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jingyu Wang Zhijuan Du Tao Sun Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

For inductive link prediction in knowledge graphs, we address the problem by considering bridging links (i.e., links connecting discrete graphs). Although current research overcomes the traditional graph topological constraints, it tends to ignore the dynamic interactions between relations and entities as well as the capture of global information. The problem of discrete and small amounts of information in real-world knowledge graphs makes it difficult for existing methods to effectively integrate global and local information and to model complex relationships between entities. To address these issues, we propose a novel meta-analogy learning framework, Ank-motor, which integrates autonomously designed dynamic graph neural networks with analogical reasoning. The dynamic graph neural network models interactions and captures global information, while the analogical inference layer integrates entity, relation, and triple-layer information to capture local semantic details. In addition, meta-learning techniques are utilized to deal with problems with small amounts of data and to enhance the model’s ability to generalize to new tasks. Numerous experiments show that Ank-motor significantly outperforms existing models on multiple benchmark datasets.

关键词： Metalearning Semantics Knowledge graphs signal processing Benchmark testing Graph neural networks Cognition data models Acoustics Speech processing

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Federated Prototype Guided Adaption for Vision-Language Models

Federated Prototype Guided Adaption for Vision-Language Mode...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Youchao Liu Dingjiang Huang School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Federated Learning (FL) is a new pivotal paradigm for decentralized training on heterogeneous data. Recently fine-tuning of Vision-Language Models (VLMs) has been extended to the federated setting to improve overall performance. Unfortunately, in this case, FL still faces two critical challenges that hinder its actual performance: data distribution heterogeneity and high resource costs brought by large VLMs. In this paper, we introduce FedPGA, a prototype-guided method, for achieving performance improvements in the federated setting for VLMs. Concretely, we design a prototype-based adapter for the vision-language model, CLIP. The lightweight adapter updates the prior knowledge encoded in CLIP to enhance its adaption capability further and avoid the effects of data distribution heterogeneity in the federated setting. Simultaneously, small-scale operations can mitigate the computational and communication burden caused by large VLMs. Our comprehensive empirical evaluations of nine diverse image classification datasets show that our method is superior to existing FL methods under VLMs.

关键词： Training Adaptation models Costs Federated learning Computational modeling Prototypes signal processing Speech processing Faces Image classification

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Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds

Unsupervised UAV 3D Trajectories Estimation with Sparse Poin...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Hanfang Liang Yizhuo Yang Jinming Hu Jianfei Yang Fen Liu Shenghai Yuan Jianghan University China Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Compact UAV systems, while advancing delivery and surveillance, pose significant security challenges due to their small size, which hinders detection by traditional methods. This paper presents a cost-effective, unsupervised UAV detection method using spatial-temporal sequence processing to fuse multiple LiDAR scans for accurate UAV tracking in real-world scenarios. Our approach segments point clouds into foreground and background, analyzes spatial-temporal data, and employs a scoring mechanism to enhance detection accuracy. Tested on a public dataset, our solution placed 4th in the CVPR 2024 UG2+ Challenge, demonstrating its practical effectiveness. We plan to open-source all designs, code and sample data for the research community @ ***/lianghanfang/UnLiDAR-UAV-Est.

关键词： Point cloud compression Accuracy Three-dimensional displays Laser radar Surveillance signal processing Autonomous aerial vehicles Trajectory Security Speech processing

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The Evolution of System on Chip Integrated Circuits for Hearing-Aid signal processing

The Evolution of System on Chip Integrated Circuits for Hear...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Ryan, James Coenen, Ivo Brennan, Robert onsemi Waterloo Canada onsemi Marin-Epagnier Switzerland

ISBN: (纸本)9798350368741

关键词： Artificial Intelligence Hearing aid signal processing System on Chip

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SSAST-Adapter: A Parameter-efficient Incremental Learning Algorithm for Underwater Acoustic Target Recognition

SSAST-Adapter: A Parameter-efficient Incremental Learning Al...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Qian Zhu Qisheng Xu Boqing Zhu Zijian Gao Lingbin Zeng Kele Xu College of Computer Science and Technology National University of Defense Technology Changsha China State Key Laboratory of Complex & Critical Software Environment Changsha China College of Meteorology and Oceanography National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Underwater acoustic target recognition involves identifying and classifying targets in underwater environments using acoustic signals. In recent years, deep learning has made significant progress in this field. However, the models require the entire dataset to be available upfront, and classification categories must be predefined. In practical scenarios, new objects or species may appear in underwater environments over time, making it impractical to retrain a model from scratch each time new data is introduced. At the same time, training on new data inevitably leads to catastrophic forgetting of past data. To address these challenges, we propose a large-scale pre-training strategy combined with adapters to enable incremental learning without the need for complete retraining. To minimize the number of parameters during model fine-tuning, we employ a adapter structure at various network layers, reducing the number of trainable parameters to less than 2%. Experimental results demonstrate that our proposed method effectively reduces the need for full retraining by allowing the model to update in a resource-efficient manner using only new data, and the model has achieved high recognition accuracy in underwater acoustic target recognition tasks.

关键词： Training Adaptation models Incremental learning Target recognition signal processing algorithms Speech recognition Transformers data models Underwater acoustics Speech processing

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